Ghost Imaging by a Proportional Parameter to Filter Bucket Data

Abstract

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.

Second-order correlation function supported optical sensing for particle detection

“…The statistics of the radiation field, or photon statistics, begun with the seminal paper by Hanbury-Brown and Twiss for stellar size measurements [1,2], has evolved to become one of the pillars of physics [3][4][5][6]. Its applications run from the detection of squeezed states and antibunching [7], ghost imaging [8,9], improving the sensitivity of gravitational wave detectors [10], particle sizing [11], dynamic light scattering [12], fluid mechanics and velocimetry [13,14], quantum information [15,16], and advanced measurement techniques [17,18], to name only a few.…”